bioRxivNeurosciencePreprint
Identification of potential proteins and microRNAs in Multiple Sclerosis and Huntington's diseases using in silico methods.
Abstract
Neurogenerative diseases like multiple sclerosis, Huntingtons disease are the major roadblocks in the way towards a healthy brain. Neurodegenerative diseases like multiple sclerosis and Huntingtons disease are affected by several factors such as environmental, immunological, genetics, and the worse scenario we can think of is that they are on the rise worldwide. Degenerative diseases specifically target a limited group of neurons at first resulting in the loss of specific functions associated with the specific part of the brain. The early diagnosis of these neurodegenerative diseases is important so that treatments can start from the early stages of these diseases. In this study, we have established a link between Multiple sclerosis and Huntingtons disease, and also we were able to establish the possible microRNAs that were connected to the expression of genes associated with these two diseases. In this present study, we analyzed the microarray datasets obtained from Gene Expression Omnibus and we identified 266 differentially expressed genes tried to identify using in silico methods the Hub genes involved in Multiple sclerosis and Huntingtons disease. After identifying the genes and proteins we tried to identify the microRNAs that are interacting with the Hub genes. In our study, we identified that the protein network has PTPRC, CXCL8, RBM25 proteins that have maximum connectivity. The top Hub genes are then subjected to a database that contains information concerning the microRNAs that are interacting with the Hub proteins as well as with each other. According to our study, the hsa-mir-155-5p has one of the highest degrees in the microRNA network. Our study will be useful in the future for the development of new drug targets for these neurodegenerative diseases.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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